Published December 2021
| Submitted + Published
Journal Article
Open
Protein sequence design with deep generative models
Chicago
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Abstract
Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.
Additional Information
© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Available online 26 May 2021. The authors wish to thank members Lucas Schaus and Sabine Brinkmann-Chen for feedback on early drafts. This work is supported by the Camille and Henry Dreyfus Foundation (ML-20-194) and the NSF Division of Chemical, Bioengineering, Environmental, and Transport Systems (1937902). The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Attached Files
Published - 1-s2.0-S136759312100051X-main.pdf
Submitted - 2104.04457.pdf
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Additional details
- Eprint ID
- 108709
- Resolver ID
- CaltechAUTHORS:20210413-080510593
- Camille and Henry Dreyfus Foundation
- ML-20-194
- NSF
- CBET-1937902
- Created
-
2021-04-13Created from EPrint's datestamp field
- Updated
-
2021-05-27Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering (BBE)